Complex networks can comprise topological features that seldom occur in simple networks but often occur in real-world networks such as computer networks, biological networks, and social networks. A complex network is said to have a community structure if the network divides into groups of nodes, wherein the groups of nodes are more densely connected internally than with the rest of the network, suggesting that the data comprises one or more natural divisions. For example, a social network can have community groups based on occupation, location, interests, education, and the like. Further, characteristics such as the small-world property, clustering, and community structure can be found in complex network data.
Finding community structures in a network can prove difficult because, for example, the number of community structures within the network may be unknown and the community networks can be of unequal size and/or density. Some existing methods can be used to discover community structures within a network, such as, for example, hierarchical clustering, the Girvan-Newman algorithm, modularity maximization, and others. While these methods can prove successful in analyzing conventional networks, these methods behave poorly when analyzing personal electronic mail (email) data.
Therefore, it may be desirable to have systems and methods for community detection in complex networks. In particular it may be desirable to have systems and methods for detecting and visualizing community traits within personal email data.